Selective Antagonism of Opioid-Induced Ventilatory Depression by an Ampakine Molecule in Humans Without Loss of Opioid Analgesia
Bibliographic record
Abstract
Ventilatory depression is a significant risk associated with the use of opioids. We assessed whether opioid-induced ventilatory depression can be selectively antagonized by an ampakine without reduction of analgesia. In 16 healthy men, after a single oral dose of 1,500 mg of the ampakine CX717, a target concentration of 100 ng/ml alfentanil decreased the respiratory frequency by only 2.9 +/- 33.4% as compared with 25.6 +/- 27.9% during placebo coadministration (P < 0.01).Blood oxygenation and the ventilatory response to hypercapnic challenge also showed significantly smaller decreases with CX717 than with placebo. In contrast, CX717 did not affect alfentanil-induced analgesia in either electrical or heat-based experimental models of pain. Both ventilatory depression and analgesia were reversed with 1.6 mg of naloxone. These results support the use of ampakines as selective antidotes in humans to counter opioid-induced ventilatory depression without affecting opioid-mediated analgesia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".